Glossary Term

Omics Data

What is Omics Data?

Large-scale biological datasets generated by high-throughput technologies that comprehensively measure molecules like DNA, RNA, proteins, or metabolites across entire biological systems.

Omics data comes from disciplines that study biological systems at the molecular level: genomics (DNA), transcriptomics (RNA), proteomics (proteins), metabolomics (metabolites), and others. Each field generates massive, complex datasets through high-throughput technologies like next-generation sequencing and mass spectrometry, producing terabytes to petabytes of raw output per research program.

Managing omics data is a serious infrastructure challenge. A single genome sequencing run can produce hundreds of gigabytes of FASTQ files. Multiply that across thousands of samples in a drug discovery pipeline or multi-site research collaboration, and the storage footprint grows fast. These files land across parallel file systems, object stores, and archive tiers, often without consistent naming conventions or lifecycle policies.

The problem gets worse with multi-omics research, where teams combine datasets from different disciplines to build richer biological models. Without a metadata-driven approach to catalog and track these files across heterogeneous storage, researchers waste time hunting for data instead of analyzing it. Organizations managing omics data at scale need visibility into file types, access patterns, and provenance across their entire unstructured data environment, turning scattered instrument outputs into FAIR-compliant, discoverable research assets.

Starfish Storage helps life sciences teams manage omics data at scale by scanning billions of files across heterogeneous storage, extracting metadata from 80+ file types (including FASTQ, BAM, and other genomics formats), and providing a searchable Unstructured Data Catalog that gives researchers self-service visibility into their data without filing an IT ticket.